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Pretrain LLMs with LitGPT

This document explains how to pretrain LLMs using LitGPT.

 

Using the litgpt pretrain command

You can pretrain models in LitGPT using the litgpt pretrain API starting with any of the available architectures listed by calling litgpt pretrain list without any additional arguments:

 

Tip

To install all required dependencies before pretraining, first run pip install "litgpt[all]".  

litgpt pretrain list

Shown below is an abbreviated list:

ValueError: Please specify --model_name <model_name>. Available values:
Camel-Platypus2-13B
...
Gemma-2b
...
Llama-2-7b-hf
...
Mixtral-8x7B-v0.1
...
pythia-14m

For demonstration purposes, we can pretrain a small 14 million-parameter Pythia model on the small TinyStories dataset using the debug.yaml config file as follows:

litgpt pretrain pythia-14m \
   --config https://raw.githubusercontent.com/Lightning-AI/litgpt/main/config_hub/pretrain/debug.yaml

 

Pretrain on custom data

The simplest way to get started with pretraining on a small custom dataset is by using the TextFiles data module, which lets you pretrain a dataset from a folder containing plain text files.

 

Note

This approach adds a beginning-of-sequence token at the beginning of each text file. However, it otherwise assumes that you have already cleaned the text files, for example, removing any unwanted characters and inserting beginning-of-sequence and end-of-sequence tokens if applicable in case a text file conists of multiple documents.

 

Warning

Using this approach is only recommended for small datasets. Since text data is highly compressible, it is often stored in compressed format, and often in file formats where documents can be loaded row by row without having to load entire files at once. In other words, this TextFiles approach is only feasible to store the data in plain text files due to the limited size. For datasets that take up multiple gigabytes, we recommend preprocessing it with LitData and then reading it from a local directory or S3 connection using --data LitData.

 

For instance, assume you stored a number of text files in a custom_pretraining_dataset folder (we recommend avoiding small files and concatenating them to files of at least 50 Mb for efficiency):

~ ls -lh custom_pretraining_data 
total 3225M
-rw-r--r-- 1 sebastian 50M Apr  2 18:31 combined_1.txt
-rw-r--r-- 1 sebastian 50M Apr  2 18:31 combined_2.txt
-rw-r--r-- 1 sebastian 50M Apr  2 18:31 combined_3.txt
-rw-r--r-- 1 sebastian 50M Apr  2 18:31 combined_4.txt
-rw-r--r-- 1 sebastian 50M Apr  2 18:31 combined_5.txt
...

You can then use the TextFiles API to pretrain a model (here a small pythia-14m model for illustration purposes) from scratch as follows:

litgpt download EleutherAI/pythia-14m \
  --tokenizer_only true

litgpt pretrain pythia-14m \
   --tokenizer_dir EleutherAI/pythia-14m \
   --data TextFiles \
   --data.train_data_path custom_pretraining_data \
   --train.lr_warmup_steps=200
   --optimizer.lr 0.005

 

Tip

Use the litgpt pretrain --data.help TextFiles command to list additional dataset options.  

 

Continued pretraining on custom data

Often, it makes sense to adopt an existing pretrained model and further pretrain it on our own custom data. The existing pretrained model can be either our own pretrained model or a model downloaded from a model hub.

The following subsections illustrate three typical scenarioes:

  1. Starting from a downloaded base model
  2. Continuing the pretraining after interruption
  3. Further pretraining on a different dataset

 

Note

This approach assumes that you have already cleaned the text files, for example, removing any unwanted characters and inserting beginning-of-sequence and end-of-sequence tokens if applicable.

 

Warning

Using this approach is only recommended for small datasets. Since text data is highly compressible, it is often stored in compressed format, and often in file formats where documents can be loaded row by row without having to load entire files at once. In other words, this TextFiles approach is only feasible to store the data in plain text files due to the limited size. For datasets that take up multiple gigabytes, we recommend preprocessing it with LitData and then reading it from a local directory or S3 connection using --data LitData --data.path path/to/your/data.

 

1) Continued pretraining when starting from a downloaded base model

For instance, let's assume we download a Pythia model:

litgpt download EleutherAI/pythia-14m

Next, assume we have a custom dataset stored in text files similar to the Pretrain on custom data above. We can further pretrain the Pythia model via the --initial_checkpoint_dir setting as follows:

litgpt pretrain pythia-160m \
   --initial_checkpoint_dir EleutherAI/pythia-160m \
   --tokenizer_dir EleutherAI/pythia-160m \
   --out_dir ./new_pretrained_checkpoint \
   --data TextFiles \
   --data.train_data_path custom_pretraining_data \
   --train.max_tokens 1_000_000

 

Tip

Use the litgpt pretrain --data.help TextFiles command to list additional dataset options.

 

2) Continued pretraining after interruption

In case a you interrupted a training run, you can continue it with the --resume option, for example:

litgpt pretrain pythia-160m \
   --resume "auto" \
   --tokenizer_dir EleutherAI/pythia-160m \
   --out_dir ./new_pretrained_checkpoint \
   --data TextFiles \
   --data.train_data_path custom_pretraining_data \
   --train.max_tokens 1_000_000

 

3) Continued pretraining on a new dataset

Suppose you pretrained a model using the examples above. To further pretrain the model on a new dataset, you first need to convert the pretrained checkpoint via the following command:

litgpt convert_pretrained_checkpoint ./new_pretrained_checkpoint/final ./new_pretrained_checkpoint_converted

Then, you can pretrain the converted model on the new dataset as follows:

litgpt pretrain pythia-160m \
   --initial_checkpoint_dir ./new_pretrained_checkpoint_converted \
   --tokenizer_dir EleutherAI/pythia-160m \
   --out_dir ./new_pretrained_checkpoint_2 \
   --data TextFiles \
   --data.train_data_path custom_pretraining_data_2 \
   --train.max_tokens 1_000_000

 

Pretrain a 1.1B TinyLlama model

You can find an end-to-end LitGPT tutorial for pretraining a TinyLlama model using LitGPT here.

 

Optimize LitGPT pretraining with Lightning Thunder

Lightning Thunder is a source-to-source compiler for PyTorch, which is fully compatible with LitGPT. In experiments, Thunder resulted in a 40% speed-up compared to using regular PyTorch when finetuning a 7B Llama 2 model.

For more information, see the Lightning Thunder extension README.

 

Project templates

The following Lightning Studio templates provide LitGPT pretraining projects in reproducible environments with multi-GPU and multi-node support:  

Prepare the TinyLlama 1T token dataset

Pretrain LLMs - TinyLlama 1.1B

Continued Pretraining with TinyLlama 1.1B